IFCIP: An integrated optimization method for planning filters in fluid power systems under uncertainty

Abstract

An interval-fuzzy chance-constrained integer programming (IFCIP) method is developed for contamination control of a fluid power system (FPS) under uncertainty. The model is derived by incorporating the techniques of fuzzy and chance-constrained programming within a general interval-optimization framework. It can tackle uncertainties presented as both fuzzy members and discrete intervals. The developed method is applied to a case of a one-year contamination control planning for FPS. Interval solutions associated different risk levels of constraint violation are obtained, which can be used for generating decision alternatives. Generally, willingness to take a higher risk of constraint violation will guarantee a lower system cost; a strong desire to acquire a lower risk will run into a higher system cost. Thus, the method provides not only decision variable solutions presented as stable intervals but also the associated risk levels in violating the system constraints.

Keywords:

• contamination control
• decision-making
• fluid power system
• interval-fuzzy
• chance-constrained
• optimization
• uncertainty

Acknowledgements

This research was funded by Natural Science Foundations of China (50675074 and 50775081), National High-tech R&D (863) Program (2006AA09Z238), NCET of State Education Ministry (NCET-07-0330) and PHR (IHLB) 20090203.